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Found 4 papers, 3 papers with code
Unravel Anomalies: An End-to-end Seasonal-Trend Decomposition Approach for Time Series Anomaly Detection
Traditional Time-series Anomaly Detection (TAD) methods often struggle with the composite nature of complex time-series data and a diverse array of anomalies.
Topic Modeling with Wasserstein Autoencoders
To measure the diversity of the produced topics, we propose a simple topic uniqueness metric.
WEAKLY SEMI-SUPERVISED NEURAL TOPIC MODELS
We extend NTMs to the weakly semi-supervised setting by using informative priors in the training objective.
Coherence-Aware Neural Topic Modeling
Topic models are evaluated based on their ability to describe documents well (i. e. low perplexity) and to produce topics that carry coherent semantic meaning.
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